





'''simple docstring'''






import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


lowerCAmelCase									=   16
lowerCAmelCase									=   32






def   __A    (				a_    :	Accelerator   ,a_    :	int = 1_6   ,a_    :	str = "bert-base-cased"						):
		lowerCAmelCase  :     Optional[int]       						= AutoTokenizer.from_pretrained(a_						)
		lowerCAmelCase  :     str       						= load_dataset("glue"   ,"mrpc"						)

		def tokenize_function(a_    :	Optional[int]						):
				# max_length=None => use the model max length (it's actually the default)
				lowerCAmelCase  :     str       						= tokenizer(examples["sentence1"]   ,examples["sentence2"]   ,truncation=a_   ,max_length=a_						)
				return outputs

		# Apply the method we just defined to all the examples in all the splits of the dataset
		lowerCAmelCase  :     List[str]       						= datasets.map(
		    a_   ,batched=a_   ,remove_columns=["idx", "sentence1", "sentence2"]   ,load_from_cache_file=a_						)

		# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
		# transformers library
		lowerCAmelCase  :     Dict       						= tokenized_datasets.rename_column("label"   ,"labels"						)

		def collate_fn(a_    :	Optional[int]						):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				if accelerator.distributed_type == DistributedType.TPU:
						return tokenizer.pad(a_   ,padding="max_length"   ,max_length=1_2_8   ,return_tensors="pt"						)
				return tokenizer.pad(a_   ,padding="longest"   ,return_tensors="pt"						)

		# Instantiate dataloaders.
		lowerCAmelCase  :     int       						= DataLoader(
		    tokenized_datasets["train"]   ,shuffle=a_   ,collate_fn=a_   ,batch_size=a_						)
		lowerCAmelCase  :     Optional[int]       						= DataLoader(
		    tokenized_datasets["validation"]   ,shuffle=a_   ,collate_fn=a_   ,batch_size=a_						)

		return train_dataloader, eval_dataloader






def   __A    (				a_    :	int   ,a_    :	List[Any]   ,a_    :	Union[str, Any]   ,a_    :	Tuple						):
		model.eval()
		lowerCAmelCase  :     Optional[int]       						= 0
		for step, batch in enumerate(a_						):
				# We could avoid this line since we set the accelerator with `device_placement=True`.
				batch.to(accelerator.device						)
				with torch.no_grad():
						lowerCAmelCase  :     Union[str, Any]       						= model(**a_						)
				lowerCAmelCase  :     str       						= outputs.logits.argmax(dim=-1						)
				# It is slightly faster to call this once, than multiple times
				lowerCAmelCase						,		lowerCAmelCase  :     List[Any]       						= accelerator.gather(
				    (predictions, batch["labels"])						)  # If we are in a multiprocess environment, the last batch has duplicates
				if accelerator.use_distributed:
						if step == len(a_						) - 1:
								lowerCAmelCase  :     Optional[Any]       						= predictions[: len(eval_dataloader.dataset						) - samples_seen]
								lowerCAmelCase  :     Any       						= references[: len(eval_dataloader.dataset						) - samples_seen]
						else:
								samples_seen += references.shape[0]
				metric.add_batch(
				    predictions=a_   ,references=a_   ,)

		lowerCAmelCase  :     int       						= metric.compute()
		return eval_metric["accuracy"]






def   __A    (				a_    :	Dict   ,a_    :	Optional[int]						):
		# Initialize accelerator
		lowerCAmelCase  :     List[Any]       						= Accelerator()

		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		lowerCAmelCase  :     int       						= config["lr"]
		lowerCAmelCase  :     List[str]       						= int(config["num_epochs"]						)
		lowerCAmelCase  :     List[str]       						= int(config["seed"]						)
		lowerCAmelCase  :     int       						= int(config["batch_size"]						)
		lowerCAmelCase  :     Any       						= args.model_name_or_path

		set_seed(a_						)
		lowerCAmelCase						,		lowerCAmelCase  :     List[Any]       						= get_dataloaders(a_   ,a_   ,a_						)

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		lowerCAmelCase  :     int       						= AutoModelForSequenceClassification.from_pretrained(a_   ,return_dict=a_						)

		# Instantiate optimizer
		lowerCAmelCase  :     Tuple       						= (
		    AdamW
		    if accelerator.state.deepspeed_plugin is None
		    or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
		    else DummyOptim
		)
		lowerCAmelCase  :     int       						= optimizer_cls(params=model.parameters()   ,lr=a_						)

		if accelerator.state.deepspeed_plugin is not None:
				lowerCAmelCase  :     Union[str, Any]       						= accelerator.state.deepspeed_plugin.deepspeed_config[
				    "gradient_accumulation_steps"
				]
		else:
				lowerCAmelCase  :     Any       						= 1
		lowerCAmelCase  :     Dict       						= (len(a_						) * num_epochs) // gradient_accumulation_steps

		# Instantiate scheduler
		if (
		    accelerator.state.deepspeed_plugin is None
		    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
		):
				lowerCAmelCase  :     Optional[Any]       						= get_linear_schedule_with_warmup(
				    optimizer=a_   ,num_warmup_steps=0   ,num_training_steps=a_   ,)
		else:
				lowerCAmelCase  :     Union[str, Any]       						= DummyScheduler(a_   ,total_num_steps=a_   ,warmup_num_steps=0						)

		# Prepare everything
		# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
		# prepare method.
		lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  :     Any       						= accelerator.prepare(
		    a_   ,a_   ,a_   ,a_   ,a_						)

		# We need to keep track of how many total steps we have iterated over
		lowerCAmelCase  :     Optional[Any]       						= 0
		# We also need to keep track of the stating epoch so files are named properly
		lowerCAmelCase  :     Tuple       						= 0
		lowerCAmelCase  :     str       						= evaluate.load("glue"   ,"mrpc"						)
		lowerCAmelCase  :     Any       						= num_epochs

		if args.partial_train_epoch is not None:
				lowerCAmelCase  :     List[str]       						= args.partial_train_epoch

		if args.resume_from_checkpoint:
				accelerator.load_state(args.resume_from_checkpoint						)
				lowerCAmelCase  :     str       						= args.resume_from_checkpoint.split("epoch_"						)[1]
				lowerCAmelCase  :     Any       						= ""
				for char in epoch_string:
						if char.isdigit():
								state_epoch_num += char
						else:
								break
				lowerCAmelCase  :     Dict       						= int(a_						) + 1
				lowerCAmelCase  :     Optional[Any]       						= evaluation_loop(a_   ,a_   ,a_   ,a_						)
				accelerator.print("resumed checkpoint performance:"   ,a_						)
				accelerator.print("resumed checkpoint's scheduler's lr:"   ,lr_scheduler.get_lr()[0]						)
				accelerator.print("resumed optimizers's lr:"   ,optimizer.param_groups[0]["lr"]						)
				with open(os.path.join(args.output_dir   ,f'''state_{starting_epoch-1}.json'''						)   ,"r"						) as f:
						lowerCAmelCase  :     Any       						= json.load(a_						)
						assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
						assert (
						    resumed_state["lr"] == lr_scheduler.get_lr()[0]
						), "Scheduler learning rate mismatch, loading from checkpoint failed"
						assert (
						    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
						), "Optimizer learning rate mismatch, loading from checkpoint failed"
						assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
						return

    # Now we train the model
		lowerCAmelCase  :     str       						= {}
		for epoch in range(a_   ,a_						):
				model.train()
				for step, batch in enumerate(a_						):
						lowerCAmelCase  :     Optional[Any]       						= model(**a_						)
						lowerCAmelCase  :     Optional[Any]       						= outputs.loss
						lowerCAmelCase  :     Dict       						= loss / gradient_accumulation_steps
						accelerator.backward(a_						)
						if step % gradient_accumulation_steps == 0:
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

						overall_step += 1
				lowerCAmelCase  :     int       						= f'''epoch_{epoch}'''
				lowerCAmelCase  :     Union[str, Any]       						= os.path.join(args.output_dir   ,a_						)
				accelerator.save_state(a_						)
				lowerCAmelCase  :     List[Any]       						= evaluation_loop(a_   ,a_   ,a_   ,a_						)
				lowerCAmelCase  :     Dict       						= accuracy
				lowerCAmelCase  :     Dict       						= lr_scheduler.get_lr()[0]
				lowerCAmelCase  :     Union[str, Any]       						= optimizer.param_groups[0]["lr"]
				lowerCAmelCase  :     Dict       						= epoch
				lowerCAmelCase  :     Dict       						= overall_step
				accelerator.print(f'''epoch {epoch}:'''   ,a_						)

				accelerator.wait_for_everyone()
				if accelerator.is_main_process:
						with open(os.path.join(args.output_dir   ,f'''state_{epoch}.json'''						)   ,"w"						) as f:
								json.dump(a_   ,a_						)






def   __A    (				):
		lowerCAmelCase  :     Optional[int]       						= argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."						)
		parser.add_argument(
		    "--model_name_or_path"   ,type=a_   ,default="bert-base-cased"   ,help="Path to pretrained model or model identifier from huggingface.co/models."   ,required=a_   ,)
		parser.add_argument(
		    "--output_dir"   ,type=a_   ,default="."   ,help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."   ,)
		parser.add_argument(
		    "--resume_from_checkpoint"   ,type=a_   ,default=a_   ,help="If the training should continue from a checkpoint folder."   ,)
		parser.add_argument(
		    "--partial_train_epoch"   ,type=a_   ,default=a_   ,help="If passed, the training will stop after this number of epochs."   ,)
		parser.add_argument(
		    "--num_epochs"   ,type=a_   ,default=2   ,help="Number of train epochs."   ,)
		lowerCAmelCase  :     Union[str, Any]       						= parser.parse_args()
		lowerCAmelCase  :     Dict       						= {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 4_2, "batch_size": 1_6}

		training_function(a_   ,a_						)


if __name__ == "__main__":
					main()










'''simple docstring'''






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCAmelCase									=   {"""configuration_mra""": ["""MRA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MraConfig"""]}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase									=   [
					    """MRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """MraForMaskedLM""",
					    """MraForMultipleChoice""",
					    """MraForQuestionAnswering""",
					    """MraForSequenceClassification""",
					    """MraForTokenClassification""",
					    """MraLayer""",
					    """MraModel""",
					    """MraPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mra import (
										    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MraForMaskedLM,
										    MraForMultipleChoice,
										    MraForQuestionAnswering,
										    MraForSequenceClassification,
										    MraForTokenClassification,
										    MraLayer,
										    MraModel,
										    MraPreTrainedModel,
										)
else:
					import sys

					lowerCAmelCase									=   _LazyModule(__name__, globals()["""__file__"""], _import_structure)



